Paper Group NANR 240
A High-Quality Gold Standard for Citation-based Tasks. A Challenge Set and Methods for Noun-Verb Ambiguity. SParse: Ko\cc University Graph-Based Parsing System for the CoNLL 2018 Shared Task. Complex Word Identification Based on Frequency in a Learner Corpus. Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-b …
A High-Quality Gold Standard for Citation-based Tasks
| Title | A High-Quality Gold Standard for Citation-based Tasks |
| Authors | Michael F{"a}rber, Alex Thiemann, er, Adam Jatowt |
| Abstract | |
| Tasks | Entity Linking, Entity Resolution |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1296/ |
| https://www.aclweb.org/anthology/L18-1296 | |
| PWC | https://paperswithcode.com/paper/a-high-quality-gold-standard-for-citation |
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A Challenge Set and Methods for Noun-Verb Ambiguity
| Title | A Challenge Set and Methods for Noun-Verb Ambiguity |
| Authors | Ali Elkahky, Kellie Webster, Daniel Andor, Emily Pitler |
| Abstract | English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97{%}+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1{%} of each other when measured on the WSJ have accuracies ranging from 57{%} to 75{%} accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89{%}, a 14{%} absolute (52{%} relative) improvement. Downstream, using just this enhanced tagger yields a 28{%} reduction in error over the prior best learned model for homograph disambiguation for textto-speech synthesis. |
| Tasks | Speech Synthesis, Text-To-Speech Synthesis, Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1277/ |
| https://www.aclweb.org/anthology/D18-1277 | |
| PWC | https://paperswithcode.com/paper/a-challenge-set-and-methods-for-noun-verb |
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SParse: Ko\cc University Graph-Based Parsing System for the CoNLL 2018 Shared Task
| Title | SParse: Ko\cc University Graph-Based Parsing System for the CoNLL 2018 Shared Task |
| Authors | Berkay {"O}nder, Can G{"u}meli, Deniz Yuret |
| Abstract | We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48{%} LAS, 78.63{%} MLAS, 78.69{%} BLEX and 81.76{%} CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78{%} LAS, 59.10{%} MLAS, 61.38{%} BLEX and 61.72{%} CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results. |
| Tasks | Dependency Parsing, Language Modelling, Meta-Learning, Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-2022/ |
| https://www.aclweb.org/anthology/K18-2022 | |
| PWC | https://paperswithcode.com/paper/sparse-koa-university-graph-based-parsing |
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Complex Word Identification Based on Frequency in a Learner Corpus
| Title | Complex Word Identification Based on Frequency in a Learner Corpus |
| Authors | Tomoyuki Kajiwara, Mamoru Komachi |
| Abstract | We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the frequency of target words in various corpora. Our simple systems performed best on 5 tracks out of 12 tracks. Our ablation analysis revealed the usefulness of a learner corpus for CWI task. |
| Tasks | Complex Word Identification, Lexical Simplification, Reading Comprehension, Text Simplification |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0521/ |
| https://www.aclweb.org/anthology/W18-0521 | |
| PWC | https://paperswithcode.com/paper/complex-word-identification-based-on |
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Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-based Modeling
| Title | Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-based Modeling |
| Authors | Hiroaki Santo, Michael Waechter, Masaki Samejima, Yusuke Sugano, Yasuyuki Matsushita |
| Abstract | We present a practical method for geometric point light source calibration. Unlike in prior works that use Lambertian spheres, mirror spheres, or mirror planes, our calibration target consists of a Lambertian plane and small shadow casters at unknown positions above the plane. Due to their small size, the casters’ shadows can be localized more precisely than highlights on mirrors. We show that, given shadow observations from a moving calibration target and a fixed camera, the shadow caster positions and the light position or direction can be simultaneously recovered in a structure from motion framework. Our evaluation on simulated and real scenes shows that our method yields light estimates that are stable and more accurate than existing techniques while having a considerably simpler setup and requiring less manual labor. |
| Tasks | Calibration |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/light-structure-from-pin-motion-simple-and |
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A Spectral Approach to Generalization and Optimization in Neural Networks
| Title | A Spectral Approach to Generalization and Optimization in Neural Networks |
| Authors | Farzan Farnia, Jesse Zhang, David Tse |
| Abstract | The recent success of deep neural networks stems from their ability to generalize well on real data; however, Zhang et al. have observed that neural networks can easily overfit random labels. This observation demonstrates that with the existing theory, we cannot adequately explain why gradient methods can find generalizable solutions for neural networks. In this work, we use a Fourier-based approach to study the generalization properties of gradient-based methods over 2-layer neural networks with sinusoidal activation functions. We prove that if the underlying distribution of data has nice spectral properties such as bandlimitedness, then the gradient descent method will converge to generalizable local minima. We also establish a Fourier-based generalization bound for bandlimited spaces, which generalizes to other activation functions. Our generalization bound motivates a grouped version of path norms for measuring the complexity of 2-layer neural networks with ReLU activation functions. We demonstrate numerically that regularization of this group path norm results in neural network solutions that can fit true labels without losing test accuracy while not overfitting random labels. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=HJBhEMbRb |
| https://openreview.net/pdf?id=HJBhEMbRb | |
| PWC | https://paperswithcode.com/paper/a-spectral-approach-to-generalization-and |
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AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
| Title | AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing |
| Authors | Tao Ji, Yufang Liu, Yijun Wang, Yuanbin Wu, Man Lan |
| Abstract | We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26). |
| Tasks | Dependency Parsing |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-2025/ |
| https://www.aclweb.org/anthology/K18-2025 | |
| PWC | https://paperswithcode.com/paper/antnlp-at-conll-2018-shared-task-a-graph |
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Hybed: Hyperbolic Neural Graph Embedding
| Title | Hybed: Hyperbolic Neural Graph Embedding |
| Authors | Benjamin Paul Chamberlain, James R Clough, Marc Peter Deisenroth |
| Abstract | Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph-based tasks, embeddings in high-dimensional Euclidean spaces have been learned. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but a negatively curved hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that hyperbolic embeddings significantly outperform Euclidean embeddings on vertex classification tasks for several real-world public datasets. |
| Tasks | Graph Embedding |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=S1xDcSR6W |
| https://openreview.net/pdf?id=S1xDcSR6W | |
| PWC | https://paperswithcode.com/paper/hybed-hyperbolic-neural-graph-embedding |
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Finding the way from "a to a: Sub-character morphological inflection for the SIGMORPHON 2018 shared task
| Title | Finding the way from "a to a: Sub-character morphological inflection for the SIGMORPHON 2018 shared task |
| Authors | Fynn Schr{"o}der, Marcel Kamlot, Gregor Billing, Arne K{"o}hn |
| Abstract | |
| Tasks | Morphological Inflection |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-3009/ |
| https://www.aclweb.org/anthology/K18-3009 | |
| PWC | https://paperswithcode.com/paper/finding-the-way-from-a-to-a-sub-character |
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Training Binary Weight Networks via Semi-Binary Decomposition
| Title | Training Binary Weight Networks via Semi-Binary Decomposition |
| Authors | Qinghao Hu, Gang Li, Peisong Wang, Yifan Zhang, Jian Cheng |
| Abstract | Recently binary weight networks have attracted lots of attentions due to their high computational efficiency and small parameter size. Yet they still suffer from large accuracy drops because of their limited representation capacity. In this paper, we propose a novel semi-binary decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix. Since the matrix product of binary matrices has more numerical values than binary matrix, the proposed semi-binary decomposition has more representation capacity. Besides, we propose an alternating optimization method to solve the semi-binary decomposition problem while keeping binary constraints. Extensive experiments on AlexNet, ResNet-18, and ResNet-50 demonstrate that our method outperforms state-of-the-art methods by a large margin (5 percentage higher in top1 accuracy). We also implement binary weight AlexNet on FPGA platform, which shows that our proposed method can achieve $sim 9 imes$ speed-ups while reducing the consumption of on-chip memory and dedicated multipliers significantly. |
| Tasks | |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Qinghao_Hu_Training_Binary_Weight_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Qinghao_Hu_Training_Binary_Weight_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/training-binary-weight-networks-via-semi |
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Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets
| Title | Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets |
| Authors | Fabian Eckert, Mariana Neves |
| Abstract | Question answering (QA) systems usually rely on advanced natural language processing components to precisely understand the questions and extract the answers. Semantic role labeling (SRL) is known to boost performance for QA, but its use for biomedical texts has not yet been fully studied. We analyzed the performance of three SRL tools (BioKIT, BIOSMILE and PathLSTM) on 1776 questions from the BioASQ challenge. We compared the systems regarding the coverage of the questions and snippets, as well as based on pre-defined criteria, such as easiness of installation, supported formats and usability. Finally, we integrated two of the tools in a simple QA system to further evaluate their performance over the official BioASQ test sets. |
| Tasks | Named Entity Recognition, Part-Of-Speech Tagging, Question Answering, Semantic Parsing, Semantic Role Labeling |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5302/ |
| https://www.aclweb.org/anthology/W18-5302 | |
| PWC | https://paperswithcode.com/paper/semantic-role-labeling-tools-for-biomedical |
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Open ASR for Icelandic: Resources and a Baseline System
| Title | Open ASR for Icelandic: Resources and a Baseline System |
| Authors | Anna Bj{"o}rk Nikul{'a}sd{'o}ttir, Inga R{'u}n Helgad{'o}ttir, Matth{'\i}as P{'e}tursson, J{'o}n Gu{\dh}nason |
| Abstract | |
| Tasks | Language Modelling, Large Vocabulary Continuous Speech Recognition, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1495/ |
| https://www.aclweb.org/anthology/L18-1495 | |
| PWC | https://paperswithcode.com/paper/open-asr-for-icelandic-resources-and-a |
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Syntactic Category Learning as Iterative Prototype-Driven Clustering
| Title | Syntactic Category Learning as Iterative Prototype-Driven Clustering |
| Authors | Jordan Kodner |
| Abstract | |
| Tasks | Part-Of-Speech Tagging |
| Published | 2018-01-01 |
| URL | https://www.aclweb.org/anthology/W18-0305/ |
| https://www.aclweb.org/anthology/W18-0305 | |
| PWC | https://paperswithcode.com/paper/syntactic-category-learning-as-iterative |
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Proceedings of The Third Workshop on Representation Learning for NLP
| Title | Proceedings of The Third Workshop on Representation Learning for NLP |
| Authors | |
| Abstract | |
| Tasks | Representation Learning |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3000/ |
| https://www.aclweb.org/anthology/W18-3000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-third-workshop-on |
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Stochastic Nonparametric Event-Tensor Decomposition
| Title | Stochastic Nonparametric Event-Tensor Decomposition |
| Authors | Shandian Zhe, Yishuai Du |
| Abstract | Tensor decompositions are fundamental tools for multiway data analysis. Existing approaches, however, ignore the valuable temporal information along with data, or simply discretize them into time steps so that important temporal patterns are easily missed. Moreover, most methods are limited to multilinear decomposition forms, and hence are unable to capture intricate, nonlinear relationships in data. To address these issues, we formulate event-tensors, to preserve the complete temporal information for multiway data, and propose a novel Bayesian nonparametric decomposition model. Our model can (1) fully exploit the time stamps to capture the critical, causal/triggering effects between the interaction events, (2) flexibly estimate the complex relationships between the entities in tensor modes, and (3) uncover hidden structures from their temporal interactions. For scalable inference, we develop a doubly stochastic variational Expectation-Maximization algorithm to conduct an online decomposition. Evaluations on both synthetic and real-world datasets show that our model not only improves upon the predictive performance of existing methods, but also discovers interesting clusters underlying the data. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7918-stochastic-nonparametric-event-tensor-decomposition |
| http://papers.nips.cc/paper/7918-stochastic-nonparametric-event-tensor-decomposition.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-nonparametric-event-tensor |
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